A nomogram model based on the combination of the systemic immune-inflammation index and prognostic nutritional index predicts weight regain after laparoscopic sleeve gastrectomy

The high rate of weight regain after laparoscopic sleeve gastrectomy is a great challenge. The systemic immune-inflammation index (SII; calculated by neutrophils, lymphocytes, and platelets) and prognostic nutritional index (PNI; calculated by albumin and lymphocytes) are widely used as prognostic factors in various diseases.The objective of this study was to investigate independent the independent risk factors associated with weight regain in patients after laparoscopic sleeve gastrectomy.A single-center retrospective study.Weight regain was defined as the percentage of increase in body weight ≥10% in comparison with the nadir weight postoperatively. Eligible patients admitted to the bariatric center of our hospital were consecutively enrolled and grouped according to the occurrence of weight regain within 5 postoperative years. Univariate and multivariate logistic regression analyses were performed to assess potential risk factors. A nomogram model containing the risk factors was then constructed and evaluated by R.A total of 217 patients were enrolled, and 87 (40.1%) patients experienced weight regain. Univariate and logistic regression analyses indicated that depression (odds ratio [OR]: 2.51, 95% confidence interval [CI]: 1.20-5.22, P = .015), psychological counseling (OR: 2.27, 95% CI: 1.20-4.33, P = .017), preoperative C-reactive protein (OR: 2.20, 95% CI: 1.18-4.13, P = .012), and combination of SII-PNI scores (OR: .45, 95% CI: .31-.67, P < .001) were 4 independent risk factors for postoperative weight regain in laparoscopic sleeve gastrectomy patients. The area under the curve of the constructed nomogram model for predicting weight regain was .706.This study concluded that the combination of the SII-PNI was an independent risk factor for weight regain and that the nomogram model based on the combination of the SII-PNI had a good predictive value.